Surrogate-assisted teaching-learning-based optimization for high-dimensional and computationally expensive problems

In this work, a surrogate-assisted teaching-learning-based optimization algorithm is presented for high-dimensional and computationally expensive black-box optimization problems. In the presented method, a two-phase searching framework is proposed to realize the exploitation of surrogates and the metaheuristic exploration. Specifically, radial basis functions are used to build the dynamically updated surrogate models. Moreover, a surrogate-assisted knowledge mining strategy is proposed to sufficiently collect valuable information in each cycle. In this strategy, multiple groups of promising points are selected from the expensive sample set to construct the subspaces and local surrogate models, from which the most potential points are captured for the subsequent updates and optimization. At the same time, a population composed of the present best expensive samples carries out the teaching/learning-based search, accelerating local convergence and promoting global exploration. In the surrogate-assisted teaching phase, both of individual and mean behaviors are considered to make learners go close to the teacher efficiently. In the surrogate-assisted learning phase, a more extensive search range is defined to improve sampling diversity. The cooperation of the surrogate-assisted knowledge mining and the modified teaching-learning-based exploration makes the new method have excellent performance on 21 benchmark cases with 30–100 design variables. Furthermore, this method is used for the shape optimization of a blended-wing-body underwater glider, and gets impressive results.